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Since the first chatbot, ELIZA, was developed way back in 1966, the technology has come a long way. It has a storied history of development, with groundbreaking milestones, from AOL’s Smarterchild to Apple’s Siri, Amazon’s Alexa, and the recent advent of ChatGPT, Claude, and other generative AI chatbots. While ELIZA’s NLP used pattern matching and substitution methodology, chatbots today are AI-driven, making them more intuitive, responsive, flexible, and commercially viable.


The term chatbots encompasses a wide range of conversational interfaces, most of them AI-driven with varying forms of engagement. As these technologies become increasingly integrated into daily life, the interfaces of interaction and customer experiences have become paramount. Chatbot UI design is now personalised to enhance UX and overall brand engagement.


Chatbot UIs face the same design challenge as any other user interface. They need to be easy, enable seamless interaction, fast, and enhance productivity. Shoddy chatbot designs are uncommonly prevalent. It often leads to abandoned conversations, customer drop-off, and brand damage. This blog will look into some of the best practices in thoughtful design to optimise chatbot UI and UX.

UI and UX for Chatbots

For long, chatbots followed a rule-based approach with predefined conversational paths. AI has accelerated a move towards more intuitive human-like chatbot models, many of which are capable of attempting the Turing Test. So, designing chatbots means creating ‘human-like’ interactions and engagements.


Today, chatbot design is a complex process. It is a convergence of complex data analytics, charting multiple customer experience pathways, user interface design, copywriting, conversational AI, MLAs and NLP. These are deployed using multiple touchpoints including digital interfaces, interactive voice response (IVR), hand gestures, facial recognition, and virtual agents.


Chatbots are a necessary engine in a brand’s marketing toolkit, reducing operational costs, increasing engagement by as much as 10%, and effectively automating many CRM processes. Their UI and UX design decisions dictate the brand’s interaction with its end-consumers and overall performance. Clear visual cues, well-designed conversational flows, personalisation, contextual understanding, and multi-sensory touchpoints are key components of a delightful user experience.

Visual Appeal and Brand Consistency

Digital creative agency, Dribble, understood the aesthetic needs of its customer’s target audience when it built a multi-tasking messenger UI for Cuberto. With background photo collages, videos and animated transitions complimenting the clean chat bubble animations, it was a perfect showcase for Cuberto’s innovative approach to product development.


Visual clarity, consistency, and brand identity are the first things that customers encounter when interacting with a chatbot. They incorporate multiple graphical, textual, and design elements such as logos, names, avatars, colour schemes, layouts, chat bubbles, and other interactive buttons. A well-designed UI should be visually appealing, easy to use, and consistent with brand identity across platforms and devices.


This means that the underlying technology and code should not be overly complex, such that it can be easily replicated across devices and networks. A visual and using consistency helps users learn faster, use the chat interface more often, and reduce cognitive load. A good aesthetic experience is memorable and can be trigger for additional customer engagement.

Conversational Flow

South Africa founded Bookmyshow, the movie ticketing app for over two billion consumers, primarily focused in South Asia, recognised early the power of WhatsApp. It harnessed WhatsApp’s reach and high engagement rate to build a thorough customer service conversational system that has generated massive revenue. It led the way to eliminate physical ticketing in movie halls in India, replacing it with a digital QR code-based system.


A natural intuitive conversational flow is at the heart of any chatbot design. Powered by natural language processing (NLP) technologies, the chatbot needs to be coded to include expansive conversation pathways, handle ambiguities, provide context, and offer appropriate responses to user prompts. For any conversational design to be effective, it should mimic human interaction with a focus on end-objective fulfilment. No design of a chatbot can work if it does not give the user what they need.


Simple, often, is the most successful philosophy when it comes to conversational flow design. Ask Google. Its Assistant is built on the back of Google’s massive data trove and has an excellent conversational flow that maintains context across multiple queries, allows follow-up questions, and seamlessly transitions to different topics.

Multimodal Interactions

When Amazon launched the voice-led virtual assistant, Alexa, along with its Echo smart speaker, on 6th November 2014, they were careful to not “tout the new device as a fully conversational computer.” It was a fully conversational chatbot; the chat was now articulated vocally.


Since then, AI, chatbots, and virtual assistants have made massive strides. Today, any interaction design must consider multimodal interactions that combine voice, text, visuals, and even gestures or touch. These diverse input and output modalities can enhance accessibility, cater to different user preferences, and provide more intuitive and immersive experiences. Major assistants like Alexa, Apple’s Siri, and others can respond to text, voice commands, screen touch, and, in some cases, even gestures, such as waving of hands. These have greatly expanded the use cases for AI in chatbots and opened new opportunities for UI designers.

Personalisation

Today, most efficient chatbot systems can adapt to individual user preferences and behaviours. They offer different levels of personalisation features such as customizable avatars, voice and language settings, and light-adjusted graphics. They can also pick up individual customer preferences, offer personalised purchase recommendations and browsing preferences, and anticipate user needs.


These customisable chatbot systems can learn from continued user interactions and update themselves. Netflix, Amazon, and other companies now use personalised recommendation engines for all their customers. Such a detailed level of personalisation can help nurture brand loyalty and trust.

Need for Specialists

There is a need for specialist UI and UX designers, as AI and chatbot technologies continue to evolve. These new-age AI-trained designers will help build, experiment, change, optimise, and evangelise innovative visual cues, conversational flows, design ethics, interactive features and more. They are essential to the continued adoption and success of AI-based chatbots.


The ATLAS Edge Executive Program in UI/UX and Digital Design will train designers in the latest AI-based models and the tools and techniques currently being deployed in the industry. The program aims to nurture creative UI/UX designers who will bridge the gap between advanced AI capabilities and intuitive human interactions.


Know more here.

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